IoT Platform using Geode and ActiveMQ

Swapnil Bawaskar@sbawaskar

sbawaskar@apache.org

Scalable IoT Platform

• Introduction • IoT • MQTT • Apache ActiveMQ Artemis • Apache Geode

• Real world use case • Q&A

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Agenda

• Devices collect and send data to brokers

• Clients process data to deliver business value

• IoT data platform considerations • Protocol • How to read • How to Analyze • How to scale

IoT

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• MQTT • Message Queuing Telemetry Transport • Based on TCP/IP • Optimized binary protocol • No type system • Provides different QoS levels • Low energy consumption

Protocol

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• Subproject of ActiveMQ • Non blocking architecture

• High Performance • Multi Protocol • Embeddable • Clustered

• Persistence • Journaled • Relational database

ActiveMQ Artemis

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Scaling

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• When dealing with large number of devices

Scaling

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Cluster• Brokers form cluster • Clients are load balanced

Scaling

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Cluster• Need to scale the

processors

Scaling

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Cluster

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• Processors do not see all data

Scaling

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Scaling

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GEODE

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What is it?

A distributed, memory-based data management platform for data oriented apps that need: • high performance, scalability, resiliency and continuous

availability • fast access to critical data set • location aware distributed data processing • event driven data architecture

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What is it?

Numbers Everyone Should Know

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L1 cache reference 0.5 ns Branch mispredict 5 ns L2 cache reference 7 ns Mutex lock/unlock 100 ns Main memory reference 100 ns Compress 1K bytes with Zippy 10,000 ns 0.01 ms Send 1K bytes over 1 Gbps network 10,000 ns 0.01 ms Read 1 MB sequentially from memory 250,000 ns 0.25 ms Round trip within same datacenter 500,000 ns 0.5 ms Disk seek 10,000,000 ns 10 ms Read 1 MB sequentially from network 10,000,000 ns 10 ms Read 1 MB sequentially from disk 30,000,000 ns 30 ms Send packet CA->Netherlands->CA 150,000,000 ns 150 ms

http://static.googleusercontent.com/media/research.google.com/en/us/people/jeff/stanford-295-talk.pdf

• 17 billion records in memory • GE Power & Water's Remote Monitoring & Diagnostics Center

• 3 TB operational data in-memory, 400 TB archived • China Railways

• 4.6 Million transactions a day / 40K transactions a second • China Railways

• 120,000 Concurrent Users • Indian Railways

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Who are the users?

World: ~7,349,000,000

~36% of the world population

Population: 1,251,695,6161,401,586,609

China RailwayCorporation

Indian Railways

• Distributed key-value store (java.util.concurrent.ConcurrentMap)

• Region due to old JSR-107 spec • Both Keys as well as Values can be domain objects

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Regions

Server 3Server 2Server 1

Key1 value1Key2 value2

Key1 value1Key2 value2

Key1 value1Key2 value2

Key1 value1Key2 value2

Partitioned

Replicated

• Deploy Function on all servers • Runs in-process with the servers

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Functions

Server 2

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Server 2Server 1 Server 3

Key1 value1

Key2 value2

Key1 value1

Key2 value2

Key1 value1

Key2 value2

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Functions

Server 2

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Server 2Server 1 Server 3

Key1 value1

Key2 value2

Key3 value1

Key4 value2

Key5 value1

Key6 value2

• Deploy Function on all servers • Runs in-process with the servers

• Object Query Language (OQL) • Similar to SQL

• SELECT DISTINCT * FROM /exampleRegion WHERE status = ‘active’

• You can drill down into domain objects • SELECT p.name FROM /person p WHERE p.pet.type=‘dino’

• You can also invoke methods on your domain objects • SELECT DISTINCT * FROM /person p WHERE p.children.size >= 2

• Joins Possible • Between Replicate regions • Between one Partitioned and Replicate regions

• SELECT portfolio1.ID, portfolio2.status FROM /exampleRegion portfolio1, /exampleRegion2 portfolio2 WHERE portfolio1.status = portfolio2.status

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Query

• Enables event-driven apps • Register a Query with the server

• SELECT * FROM /tradeOrder t WHERE t.symbol=‘VMW’ AND t.price > 100.00

• The server then notifies when the query condition is met • Client implements the CqListener callback

• HA support • Domain objects not required on the server’s class-path

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Continuous Query

Fixed or flexible schema?

id name age pet_id

or

{id:1,name:“Fred”,age:42,pet:{name:“Barney”,type:“dino”}}

C#, C++, Java, JSON

No IDL, no schemas, no hand-coding Schema evolution (Forward and Backward Compatible)

* domain object classes not required No need to bring down cluster when domain objects change

|header|data||pdx|length|dsid|typeid|fields|offsets|

Portable Data eXchange

Efficient for queries

{id:1,name:“Fred”,age:42,pet:{name:“Barney”,type:“dino”}}

SELECTp.nameFROM/PersonpWHEREp.pet.type=“dino”

single field deserialization

But how fast is it?

Benchmark: https://github.com/eishay/jvm-serializers

Schema evolutionMember A Member B

Distributed Type Definitions

v2v1

Application #1

Application #2

v2 objects preserve data from missing fields

v1 objects use default values to fill in new fields

PDX provides forwards and backwards compatibility, no code required

• Telemetry data from machines • Predicting failure

• Outside one standard deviation • Evaluating markov model

• Use functions to iterate over data • Use CQs to notify • Update CQs based on function results

IoT Use Case

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Questions?

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